让动画分层控制更精细，支持专业与业余用户自由编辑
LayerAnimate: Layer-level Control for Animation
- 设计分层感知的扩散模型，实现对动画各图层的独立操控
- 通过自动分割与运动聚类构建高质量训练数据集，解决资源稀缺问题
- 在质量、精度和易用性上超越现有方法，适合动画创作者使用
传统动画制作将视觉元素拆分为独立图层，以支持草图、精修、上色和补间等操作。现有动漫生成视频方法通常将动画视为与真实视频不同的数据领域，缺乏图层级别的细粒度控制。为此，我们提出 LayerAnimate，一种具备分层感知架构的新型视频扩散框架，可通过图层级控制实现动画元素的精准操纵。由于专业动画资源具有商业敏感性，构建分层感知框架面临数据稀缺挑战。为此，我们提出包含自动元素分割和基于运动的层次化合并的数据整理流程。通过定量与定性对比及用户研究，我们证明 LayerAnimate 在动画质量、控制精度和可用性方面均优于现有方法，为专业动画师与业余爱好者提供高效创作工具。该框架拓展了分层动画应用的可能性，提升了创作灵活性。代码已开源：https://layeranimate.github.io。
原文摘要 · Abstract (English)
Traditional animation production decomposes visual elements into discrete layers to enable independent processing for sketching, refining, coloring, and in-betweening. Existing anime generation video methods typically treat animation as a distinct data domain different from real-world videos, lacking fine-grained control at the layer level. To bridge this gap, we introduce LayerAnimate, a novel video diffusion framework with layer-aware architecture that empowers the manipulation of layers through layer-level controls. The development of a layer-aware framework faces a significant data scarcity challenge due to the commercial sensitivity of professional animation assets. To address the limitation, we propose a data curation pipeline featuring Automated Element Segmentation and Motion-based Hierarchical Merging. Through quantitative and qualitative comparisons, and user study, we demonstrate that LayerAnimate outperforms current methods in terms of animation quality, control precision, and usability, making it an effective tool for both professional animators and amateur enthusiasts. This framework opens up new possibilities for layer-level animation applications and creative flexibility. Our code is available at https://layeranimate.github.io.
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